{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T22:00:30Z","timestamp":1789682430524,"version":"4.0.1"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,3,2]],"date-time":"2025-03-02T00:00:00Z","timestamp":1740873600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,3,2]],"date-time":"2025-03-02T00:00:00Z","timestamp":1740873600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-025-07066-4","type":"journal-article","created":{"date-parts":[[2025,3,2]],"date-time":"2025-03-02T06:47:54Z","timestamp":1740898074000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["PointUltra: ultra-efficient mamba framework for transformative point cloud analysis"],"prefix":"10.1007","volume":"81","author":[{"given":"Bo","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shengwei","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Long","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhezhe","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jialun","family":"Lv","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,3,2]]},"reference":[{"issue":"5","key":"7066_CR1","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1007\/s00138-023-01434-x","volume":"34","author":"R Cheng","year":"2023","unstructured":"Cheng R, Zeng H, Zhang B, Wang X, Zhao T (2023) FFA-Net: fast feature aggregation network for 3D point cloud segmentation. Mach Vis Appl 34(5):80","journal-title":"Mach Vis Appl"},{"key":"7066_CR2","doi-asserted-by":"crossref","unstructured":"Sun L, Li Y, Qin W (2024) PEPillar: a point-enhanced pillar network for efficient 3D object detection in autonomous driving. Vis Comput 1\u201312","DOI":"10.1007\/s00371-024-03481-5"},{"key":"7066_CR3","unstructured":"Zhu L, Liao B, Zhang Q, Wang X, Liu W, Wang X (2024) Vision mamba: efficient visual representation learning with bidirectional state space model. Preprint at arXiv:2401.09417"},{"key":"7066_CR4","doi-asserted-by":"crossref","unstructured":"Chatterjee J, Vega MT (2024) 3D-scene-former: 3D scene generation from a single RGB image using transformers. Vis Comput 1\u201315","DOI":"10.1007\/s00371-024-03573-2"},{"key":"7066_CR5","doi-asserted-by":"crossref","unstructured":"Yu X, Tang L, Rao Y, Huang T, Zhou J, Lu J (2022) Point-Bert: pre-training 3d point cloud transformers with masked point modeling. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp 19313\u201319322","DOI":"10.1109\/CVPR52688.2022.01871"},{"key":"7066_CR6","doi-asserted-by":"crossref","unstructured":"Pang Y, Wang W, Tay FE, Liu W, Tian Y, Yuan L (2022) Masked autoencoders for point cloud self-supervised learning. In: European Conference on Computer Vision. Springer, pp 604\u2013621","DOI":"10.1007\/978-3-031-20086-1_35"},{"key":"7066_CR7","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems. vol 30"},{"key":"7066_CR8","unstructured":"Yang Y, Xing Z, Zhu L (2024) Vivim: a video vision mamba for medical video object segmentation. Preprint at arXiv:2401.14168"},{"key":"7066_CR9","first-page":"572","volume":"34","author":"A Gu","year":"2021","unstructured":"Gu A, Johnson I, Goel K, Saab K, Dao T, Rudra A, R\u00e9 C (2021) Combining recurrent, convolutional, and continuous-time models with linear state space layers. Adv Neural Inf Process Syst 34:572\u2013585","journal-title":"Adv Neural Inf Process Syst"},{"key":"7066_CR10","unstructured":"He W, Han K, Tang Y, Wang C, Yang Y, Guo T, Wang Y (2024) Densemamba: State space models with dense hidden connection for efficient large language models. Preprint at arXiv:2403.00818"},{"key":"7066_CR11","unstructured":"Gu A, Goel K, Re C (2021) Efficiently modeling long sequences with structured state spaces. In: Proceedings of the International Conference on Learning Representations"},{"key":"7066_CR12","doi-asserted-by":"crossref","unstructured":"Wu C, Zheng J, Pfrommer J, Beyerer J (2023) Attention-based point cloud edge sampling. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp 5333\u20135343","DOI":"10.1109\/CVPR52729.2023.00516"},{"key":"7066_CR13","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S et al (2021) An image is worth 16x16 words: transformers for image recognition at scale. In: Proceedings of the International Conference on Learning Representations"},{"key":"7066_CR14","unstructured":"Liang D, Zhou X, Wang X, Zhu X, Xu W, Zou Z, Ye X, Bai X (2024) Pointmamba: a simple state space model for point cloud analysis. Preprint at arXiv:2402.10739"},{"key":"7066_CR15","unstructured":"Qi CR, Su H, Mo K, Guibas LJ (2017) Pointnet: deep learning on point sets for 3d classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp 652\u2013660"},{"key":"7066_CR16","doi-asserted-by":"publisher","first-page":"5903","DOI":"10.1109\/TMM.2022.3200851","volume":"25","author":"T Huang","year":"2022","unstructured":"Huang T, Zou H, Cui J, Zhang J, Yang X, Li L, Liu Y (2022) Adaptive recurrent forward network for dense point cloud completion. IEEE Trans Multimed 25:5903\u20135915","journal-title":"IEEE Trans Multimed"},{"key":"7066_CR17","doi-asserted-by":"crossref","unstructured":"Deng X, Zhang W, Ding Q, Zhang X (2023) PointVector: a vector representation in point cloud analysis. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp 9455\u20139465","DOI":"10.1109\/CVPR52729.2023.00912"},{"issue":"6","key":"7066_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3550454.3555457","volume":"41","author":"T Huang","year":"2022","unstructured":"Huang T, Zhang J, Chen J, Ding Z, Tai Y, Zhang Z, Wang C, Liu Y (2022) 3QNet: 3d point cloud geometry quantization compression network. ACM Trans Graph (TOG) 41(6):1\u201313","journal-title":"ACM Trans Graph (TOG)"},{"key":"7066_CR19","doi-asserted-by":"crossref","unstructured":"Huang T, Ding Z, Zhang J, Tai Y, Zhang Z, Chen M, Wang C, Liu Y (2023) Learning to measure the point cloud reconstruction loss in a representation space. In: CVPR. p 3","DOI":"10.1109\/CVPR52729.2023.01175"},{"key":"7066_CR20","unstructured":"Qi CR, Yi L, Su H, Guibas LJ (2017) Pointnet++: Deep hierarchical feature learning on point sets in a metric space. In: Advances in neural information processing systems. vol 30"},{"key":"7066_CR21","doi-asserted-by":"crossref","unstructured":"Choy C, Gwak J, Savarese S (2019) 4d spatio-temporal convnets: Minkowski convolutional neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp 3075\u20133084","DOI":"10.1109\/CVPR.2019.00319"},{"key":"7066_CR22","first-page":"1","volume":"31","author":"Y Li","year":"2018","unstructured":"Li Y, Bu R, Sun M, Wu W, Di X, Chen B (2018) PointCNN: convolution on X-transformed points. Adv Neural Inf Process Syst 31:1\u201337815","journal-title":"Adv Neural Inf Process Syst"},{"key":"7066_CR23","doi-asserted-by":"crossref","unstructured":"Zhao H, Jiang L, Fu C-W, Jia J (2019) Pointweb: Enhancing local neighborhood features for point cloud processing. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5565\u20135573","DOI":"10.1109\/CVPR.2019.00571"},{"key":"7066_CR24","doi-asserted-by":"crossref","unstructured":"Komarichev A, Zhong Z, Hua J (2019) A-CNN: annularly convolutional neural networks on point clouds. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp 7421\u20137430","DOI":"10.1109\/CVPR.2019.00760"},{"key":"7066_CR25","first-page":"23192","volume":"35","author":"G Qian","year":"2022","unstructured":"Qian G, Li Y, Peng H, Mai J, Hammoud H, Elhoseiny M, Ghanem B (2022) PointNeXt: revisiting PointNet++ with improved training. Adv Neural Inf Process Syst 35:23192\u201323204","journal-title":"Adv Neural Inf Process Syst"},{"key":"7066_CR26","doi-asserted-by":"crossref","unstructured":"Wu W, Qi Z, Fuxin L (2019) PointConv: deep convolutional networks on 3D point clouds. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp 9621\u20139630","DOI":"10.1109\/CVPR.2019.00985"},{"key":"7066_CR27","doi-asserted-by":"crossref","unstructured":"He Q, Wang Z, Zeng H, Zeng Y, Liu Y (2022) Svga-net: sparse voxel-graph attention network for 3d object detection from point clouds. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol 36, pp 870\u2013878","DOI":"10.1609\/aaai.v36i1.19969"},{"key":"7066_CR28","doi-asserted-by":"crossref","unstructured":"Landrieu L, Simonovsky M (2018) Large-scale point cloud semantic segmentation with superpoint graphs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp 4558\u20134567","DOI":"10.1109\/CVPR.2018.00479"},{"key":"7066_CR29","doi-asserted-by":"crossref","unstructured":"Shen Y, Feng C, Yang Y, Tian D (2018) Mining point cloud local structures by kernel correlation and graph pooling. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp 4548\u20134557","DOI":"10.1109\/CVPR.2018.00478"},{"issue":"5","key":"7066_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3326362","volume":"38","author":"Y Wang","year":"2019","unstructured":"Wang Y, Sun Y, Liu Z, Sarma SE, Bronstein MM, Solomon JM (2019) Dynamic graph CNN for learning on point clouds. ACM Trans Graph (TOG) 38(5):1\u201312 (1, 3, 7, 8)","journal-title":"ACM Trans Graph (TOG)"},{"key":"7066_CR31","doi-asserted-by":"crossref","unstructured":"Hou Y, Zhu X, Ma Y, Loy CC, Li Y (2022) Point-to-voxel knowledge distillation for lidar semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp 8479\u20138488","DOI":"10.1109\/CVPR52688.2022.00829"},{"key":"7066_CR32","unstructured":"Smith JT, Warrington A, Linderman S (2022) Simplified state space layers for sequence modeling. In: Proceedings of the International Conference on Learning Representations"},{"key":"7066_CR33","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1007\/s41095-021-0229-5","volume":"7","author":"M-H Guo","year":"2021","unstructured":"Guo M-H, Cai J-X, Liu Z-N, Mu T-J, Martin RR, Hu S-M (2021) Pct: point cloud transformer. Comput Vis Media 7:187\u2013199","journal-title":"Comput Vis Media"},{"key":"7066_CR34","doi-asserted-by":"crossref","unstructured":"Yan X, Zheng C, Li Z, Wang S, Cui S (2020) Pointasnl: robust point clouds processing using nonlocal neural networks with adaptive sampling. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp 5589\u20135598","DOI":"10.1109\/CVPR42600.2020.00563"},{"issue":"3","key":"7066_CR35","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1007\/s41095-022-0274-8","volume":"8","author":"W Wang","year":"2022","unstructured":"Wang W, Xie E, Li X, Fan D-P, Song K, Liang D, Lu T, Luo P, Shao L (2022) Pvt v2: improved baselines with pyramid vision transformer. Comput Vis Media 8(3):415\u2013424","journal-title":"Comput Vis Media"},{"key":"7066_CR36","first-page":"33330","volume":"35","author":"X Wu","year":"2022","unstructured":"Wu X, Lao Y, Jiang L, Liu X, Zhao H (2022) Point transformer v2: grouped vector attention and partition-based pooling. Adv Neural Inf Process Syst 35:33330\u201333342","journal-title":"Adv Neural Inf Process Syst"},{"key":"7066_CR37","doi-asserted-by":"crossref","unstructured":"Park C, Jeong Y, Cho M, Park J (2022) Fast point transformer. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp 16949\u201316958","DOI":"10.1109\/CVPR52688.2022.01644"},{"key":"7066_CR38","doi-asserted-by":"crossref","unstructured":"Fan L, Pang Z, Zhang T, Wang Y-X, Zhao H, Wang F, Wang N, Zhang Z (2022) Embracing single stride 3d object detector with sparse transformer. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp 8458\u20138468","DOI":"10.1109\/CVPR52688.2022.00827"},{"key":"7066_CR39","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp 10012\u201310022","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"7066_CR40","doi-asserted-by":"crossref","unstructured":"Sun P, Tan M, Wang W, Liu C, Xia F, Leng Z, Anguelov D (2022) Swformer: sparse window transformer for 3d object detection in point clouds. In: European Conference on Computer Vision. Springer, pp 426\u2013442","DOI":"10.1007\/978-3-031-20080-9_25"},{"key":"7066_CR41","unstructured":"Gu A, Dao T (2023) Mamba: linear-time sequence modeling with selective state spaces. Preprint at arXiv:2312.00752"},{"key":"7066_CR42","unstructured":"Zhu L, Liao B, Zhang Q, Wang X, Liu W, Wang X (2024) Vision mamba: efficient visual representation learning with bidirectional state space model. Preprint at arXiv:2401.09417"},{"key":"7066_CR43","unstructured":"Wang C, Tsepa O, Ma J, Wang B (2024) Graph-mamba: towards long-range graph sequence modeling with selective state spaces. Preprint at arXiv:2402.00789"},{"key":"7066_CR44","doi-asserted-by":"crossref","unstructured":"Li K, Li X, Wang Y, He Y, Wang Y, Wang L, Qiao Y (2024) Videomamba: state space model for efficient video understanding. Preprint at arXiv:2403.06977","DOI":"10.1007\/978-3-031-73347-5_14"},{"key":"7066_CR45","unstructured":"Gu A, Dao T, Ermon S, Rudra A, R\u00e9 C (2020) Hippo: recurrent memory with optimal polynomial projections. In: Advances in neural information processing systems"},{"key":"7066_CR46","unstructured":"Gu A, Goel K, R\u00e9 C (2021) Efficiently modeling long sequences with structured state spaces. Preprint at arXiv:2111.00396"},{"key":"7066_CR47","first-page":"2846","volume":"35","author":"E Nguyen","year":"2022","unstructured":"Nguyen E, Goel K, Gu A, Downs G, Shah P, Dao T, Baccus S, R\u00e9 C (2022) S4ND: modeling images and videos as multidimensional signals with state spaces. Adv Neural Inf Process Syst 35:2846\u20132861","journal-title":"Adv Neural Inf Process Syst"},{"key":"7066_CR48","first-page":"22982","volume":"35","author":"A Gupta","year":"2022","unstructured":"Gupta A, Gu A, Berant J (2022) Diagonal state spaces are as effective as structured state spaces. Adv Neural Inf Process Syst 35:22982\u201322994","journal-title":"Adv Neural Inf Process Syst"},{"key":"7066_CR49","first-page":"35971","volume":"35","author":"A Gu","year":"2022","unstructured":"Gu A, Goel K, Gupta A, R\u00e9 C (2022) On the parameterization and initialization of diagonal state space models. Adv Neural Inf Process Syst 35:35971\u201335983","journal-title":"Adv Neural Inf Process Syst"},{"key":"7066_CR50","unstructured":"Smith JT, Warrington A, Linderman SW (2022) Simplified state space layers for sequence modeling. Preprint at arXiv:2208.04933"},{"key":"7066_CR51","unstructured":"Hasani R, Lechner M, Wang T-H, Chahine M, Amini A, Rus D (2022) Liquid structural state-space models. Preprint at arXiv:2209.12951"},{"key":"7066_CR52","doi-asserted-by":"crossref","unstructured":"Behrouz A, Hashemi F (2024) Graph mamba: towards learning on graphs with state space models. Preprint at arXiv:2401.14168","DOI":"10.1145\/3637528.3672044"},{"key":"7066_CR53","doi-asserted-by":"crossref","unstructured":"Uy MA, Pham Q-H, Hua B-S, Nguyen T, Yeung S-K (2019) Revisiting point cloud classification: A new benchmark dataset and classification model on real-world data. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp 1588\u20131597","DOI":"10.1109\/ICCV.2019.00167"},{"key":"7066_CR54","unstructured":"Wu Z, Song S, Khosla A, Yu F, Zhang L, Tang X, Xiao J (2015) 3d shapenets: a deep representation for volumetric shapes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp 1912\u20131920"},{"issue":"6","key":"7066_CR55","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2980179.2980238","volume":"35","author":"L Yi","year":"2016","unstructured":"Yi L, Kim VG, Ceylan D, Shen I-C, Yan M, Su H, Lu C, Huang Q, Sheffer A, Guibas L (2016) A scalable active framework for region annotation in 3d shape collections. ACM Trans Graph (ToG) 35(6):1\u201312","journal-title":"ACM Trans Graph (ToG)"},{"key":"7066_CR56","first-page":"27061","volume":"35","author":"R Zhang","year":"2022","unstructured":"Zhang R, Guo Z, Gao P, Fang R, Zhao B, Wang D, Qiao Y, Li H (2022) Point-M2AE: multi-scale masked autoencoders for hierarchical point cloud pre-training. Adv Neural Inf Process Syst 35:27061\u201327074","journal-title":"Adv Neural Inf Process Syst"},{"key":"7066_CR57","doi-asserted-by":"crossref","unstructured":"Zhao H, Jiang L, Jia J, Torr PH, Koltun V (2021) Point transformer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp 16259\u201316268","DOI":"10.1109\/ICCV48922.2021.01595"},{"key":"7066_CR58","doi-asserted-by":"crossref","unstructured":"Hamdi A, Giancola S, Ghanem B (2021) MVTN: multi-view transformation network for 3d shape recognition. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp 1\u201311","DOI":"10.1109\/ICCV48922.2021.00007"},{"key":"7066_CR59","unstructured":"Ma X, Qin C, You H, Ran H, Fu Y (2022) Rethinking network design and local geometry in point cloud: a simple residual MLP framework. Preprint at arXiv:2202.07123"},{"key":"7066_CR60","unstructured":"Z, Wang Z, Chen Z, Wu Y et al (2024) Pointramba: a hybrid transformer-mamba framework for point cloud analysis. Preprint at arXiv:2405.15463"},{"key":"7066_CR61","unstructured":"Zhang T, Li X, Yuan H, Ji S, Yan S (2024) Point cloud mamba: point cloud learning via state space model. Preprint at arXiv:2403.00762"},{"key":"7066_CR62","doi-asserted-by":"crossref","unstructured":"Han X, Tang Y, Wang Z, Li X (2024) Mamba3D: enhancing local features for 3d point cloud analysis via state space model. Preprint at arXiv:2404.14966","DOI":"10.1145\/3664647.3681173"},{"key":"7066_CR63","doi-asserted-by":"crossref","unstructured":"Liao Y, Xie J, Geiger A (2022) KITTI-360: a novel dataset and benchmarks for urban scene understanding in 2d and 3d. Pattern Anal Mach Intell (PAMI)","DOI":"10.1109\/TPAMI.2022.3179507"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07066-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-025-07066-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07066-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,2]],"date-time":"2025-03-02T06:48:19Z","timestamp":1740898099000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-025-07066-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,2]]},"references-count":63,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["7066"],"URL":"https:\/\/doi.org\/10.1007\/s11227-025-07066-4","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,2]]},"assertion":[{"value":"13 February 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 March 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"562"}}